Development Environment, Toolchain, and AI-Assisted Workflows
Set up and maintain a professional environment: editor, AI coding assistants, version control, and the workflow around them.
AI-Assisted Software Development
PublishingThis department publishes coverage today — every story links out to its own source.
What this department watches
This department watches how software actually gets built once an AI assistant is inside the loop: the coding models and agents, the editors and command-line tools that host them, and the review, testing and deployment practice teams are rewriting to keep pace. It follows what holds up in production over what reads well in a launch post — the interesting evidence is what a team reports six months after adoption, not on the day of it.
- Coding models and agent frameworks, and what they measurably do
- Editors, command-line tools, and the toolchain around them
- Review, testing and correctness practice under AI authorship
- Deployment, reliability, and incident practice
- Security of AI-written and AI-assisted code
- What engineering teams report after adoption, not before
The questions it keeps open
These are the open questions this department works. They are questions, not conclusions — what comes back gets published with its sources attached.
- When AI generates a large share of a change, what does responsible code review look like — and what does the reviewer have to read line by line versus verify by behaviour?
- Which parts of the toolchain are consolidating and which are still churning, so a builder knows what is safe to build a workflow on?
- What failure modes do AI-assisted codebases actually hit in production — and are they new failures or old ones arriving faster?
- How do testing and debugging practice change when the code's author can't explain its own reasoning?
- What does a hiring manager or a paying client now treat as proof of engineering capability, when “wrote the code” no longer distinguishes anyone?
- Where does agent autonomy stop paying — the point past which supervising the work costs more than doing it?
How the research runs
1 · Read the real sources
AIU's research agents work from a published register of trusted sources rather than from memory — the register itself is open, so you can see what is being read and how often it is checked.
2 · Carry the method and the source
A finding is only useful if you can check it. Every item carries the source it came from and the date that source published, and links straight out to the original — so a claim can be verified rather than taken on trust.
3 · Feed the coursework
What this department reads is what keeps the coursework current — and the coursework decides what is worth watching next. The two are meant to inform each other, which is why the curriculum below is on this page rather than somewhere else.
The curriculum behind it
The major
AI-Assisted Software Development
Build software with the current generation of AI coding tools — the self-directed builder's alternative to a CS degree.
Feature Development — From Design Doc to Production
Own a feature from inception to deployment: technical design, full-stack implementation, database migrations, release.
Deployment, Infrastructure, and Production Reliability
Deploy, monitor and maintain what you build: hosting, DNS and certificates, monitoring, and incident response.
Follow this department
This department is publishing now — read the edition, or put your name down to be told when its research goes further.
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